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STH-Bass: A Spatial-Temporal Heterogeneous Bass Model to Predict Single-Tweet Popularity

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9643)

Abstract

Prediction in social networks attracts more and more attentions since social networks have become an important part of people’s lives. Although a few topic or event prediction models have been proposed in the past few years, researches that focus on the single tweet prediction just emerge recently. In this paper, we propose STH-Bass, a Spatial and Temporal Heterogeneous Bass model derived from economic field, to predict the popularity of a single tweet. Leveraging only the first day’s information after a tweet is posted, STH-Bass can not only predict the trend of a tweet with favorite count and retweet count, but also classify whether the tweet will be popular in the future. We perform extensive experiments to evaluate the efficiency and accuracy of STH-Bass based on real-world Twitter data. The evaluation results show that STH-Bass obtains much less APE than the baselines when predicting the trend of a single tweet, and an average of 24 % higher precision when classifying the tweets popularity.

Keywords

The Bass model Predicting popularity Social network 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Data Science, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Information SystemsThe University of Texas at DallasRichardsonUSA

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